TY - JOUR
T1 - Using post-classifiers to enhance fusion of low-and high-level speaker recognition
AU - Solewicz, Yosef A
AU - Koppel, M.
PY - 2007
Y1 - 2007
N2 - This paper proposes a method for automatic correction of bias in speaker recognition systems, especially fusion-based systems. The method is based on a post-classifier which learns the relative performance obtained by the constituent systems in key trials, given the training and testing conditions in which they occurred. These conditions generally reflect train/test mismatch in factors such as channel, noise, speaker stress, etc. Results obtained with several state-of-the-art systems showed up to 20% decrease in EER compared to ordinary fusion in the NIST'05 Speaker Recognition Evaluation.
AB - This paper proposes a method for automatic correction of bias in speaker recognition systems, especially fusion-based systems. The method is based on a post-classifier which learns the relative performance obtained by the constituent systems in key trials, given the training and testing conditions in which they occurred. These conditions generally reflect train/test mismatch in factors such as channel, noise, speaker stress, etc. Results obtained with several state-of-the-art systems showed up to 20% decrease in EER compared to ordinary fusion in the NIST'05 Speaker Recognition Evaluation.
UR - https://scholar.google.co.il/scholar?q=Using+Post-Classifiers+to+Enhance+Fusion+of+Low+and+High-Level+Speaker+Recognition+&btnG=&hl=en&as_sdt=0%2C5
M3 - Article
VL - 15
SP - 2063
EP - 2071
JO - IEEE Transactions on Audio, Speech, and Language Processing
JF - IEEE Transactions on Audio, Speech, and Language Processing
IS - 7
ER -